Turn off MathJax
Article Contents
Xiangyu DENG, Aijia ZHANG, Jinhong YE, “An Algorithm of Deformation Image Correction Based on Spatial Mapping,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2022.00.443
Citation: Xiangyu DENG, Aijia ZHANG, Jinhong YE, “An Algorithm of Deformation Image Correction Based on Spatial Mapping,” Chinese Journal of Electronics, vol. x, no. x, article no. , xxxx doi: 10.23919/cje.2022.00.443

An Algorithm of Deformation Image Correction Based on Spatial Mapping

doi: 10.23919/cje.2022.00.443
Funds:  This work was supported by the National Natural Science Foundation of China (61961037) and Industrial Support Plan of Education Department of Gansu Province (2021CYZC-30).
More Information
  • Author Bio:

    Xiangyu DENG (corresponding author) was born in Gansu Province, China, he is currently a Professor with the College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou. His current research interests include digital image processing, artificial neural networks, and pattern recognition. (Email: dengxy000@126.com)

    Aijia ZHANG was born in Hebei Province, China, she is currently a M.S. candidate in the School of Physics and Electronic Engineering, Northwest Normal University. Her current research interest is digital image processing. (Email: zaj_serendipity@126.com)

    Jinhong YE was born in Guangzhou Province, China, he is currently a M.S. candidate in the School of Physics and Electronic Engineering, Northwest Normal University. His current research interests are digital image processing and artificial intelligence. (Email: yejh000@126.com)

  • Available Online: 2023-08-22
  • The original image undergoes geometric deformation in terms of position, shape, size, and orientation due to the shooting angle or capturing process during image acquisition. This brings about inconveniences and significant challenges in various image processing fields such as image fusion, denoising, recognition, and segmentation. To enhance the processing ability and recognition accuracy of deformation images, an adaptive image deformity correction algorithm is proposed for quadrilaterals and triangles. The deformation image undergoes preprocessing, and the contour of the image edge is extracted. Discrete points on the image edge are identified to accurately locate the edges. The deformation of the quadrilateral or triangle is transformed into a standard rectangular or equilateral triangular image using the proposed three-dimensional homography transformation algorithm. This effectively completes the conversion from an irregular image to a regular image in an adaptive manner. Numerous experiments demonstrate that the proposed algorithm surpasses traditional methods like Hough transform and Radon transform. It improves the effectiveness of correcting deformation in images, effectively addresses the issue of geometric deformation, and provides a new technical method for processing deformation images.
  • loading
  • [1]
    Z. L. Su, L. Lu, F. J. Yang, et al., “Geometry constrained correlation adjustment for stereo reconstruction in 3D optical deformation measurements,” Optics Express, vol. 28, no. 8, pp. 12219–12232, 2020. doi: 10.1364/OE.392248
    L. Ma, “Research on distance education image correction based on digital image processing technology,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 1, article no. 18, 2019. doi: 10.1186/s13640-019-0416-9
    D. M. Feng, M. Q. Feng, E. Ozer, et al., “A vision-based sensor for noncontact structural displacement measurement,” Sensors, vol. 15, no. 7, pp. 16557–16575, 2015. doi: 10.3390/s150716557
    M. Alipour, S. J. Washlesky, and D. K. Harris, “Field deployment and laboratory evaluation of 2D digital image correlation for deflection sensing in complex environments,” Journal of Bridge Engineering, vol. 24, no. 4, article no. 04019010, 2019. doi: 10.1061/(ASCE)BE.1943-5592.0001363
    W. Li, X. Zhang, and Z. R. Wang, “Music content authentication based on beat segmentation and fuzzy classification,” EURASIP Journal on Audio, Speech, and Music Processing, vol. 2013, no. 1, article no. 11, 2013. doi: 10.1186/1687-4722-2013-11
    R. A. Rakow-Penner, N. S. White, D. J. A. Margolis, et al., “Prostate diffusion imaging with distortion correction,” Magnetic Resonance Imaging, vol. 33, no. 9, pp. 1178–1181, 2015. doi: 10.1016/j.mri.2015.07.006
    B. Fu, H. Guo, X. L. Zhao, et al., “Motion-blurred SIFT invariants based on sampling in image deformation space and univariate search,” IET Computer Vision, vol. 10, no. 7, pp. 709–717, 2016. doi: 10.1049/iet-cvi.2015.0076
    X. Y. Deng, Y. N. Zhang, and Y. H. Yang, “A shape recognition algorithm for traffic sign identification,” Computer Engineering and Science, vol. 43, no. 2, pp. 322–328, 2021. (in Chinese) doi: 10.3969/j.issn.1007-130X.2021.02.017
    L. L. Li and H. B. Ma, “Pulse coupled neural network-based multimodal medical image fusion via guided filtering and WSEML in NSCT domain,” Entropy, vol. 23, no. 5, article no. 591, 2021. doi: 10.3390/e23050591
    Y. Mo, X. D. Kang, P. H. Duan, et al., “Attribute filter based infrared and visible image fusion,” Information Fusion, vol. 75, pp. 41–54, 2021. doi: 10.1016/j.inffus.2021.04.005
    B. Meher, S. Agrawal, R. Panda, et al., “A survey on region based image fusion methods,” Information Fusion, vol. 48, pp. 119–132, 2019. doi: 10.1016/j.inffus.2018.07.010
    B. Y. Liu, L. D. Wu, H. X. Hao, et al., “Interferometric phase image denoising method via residual learning,” Journal of Electronic Imaging, vol. 30, no. 2, article no. 023013, 2021. doi: 10.1117/1.JEI.30.2.023013
    X. H. Yang, Y. Xu, Y. H. Quan, et al., “Image denoising via sequential ensemble learning,” IEEE Transactions on Image Processing, vol. 29, pp. 5038–5049, 2020. doi: 10.1109/TIP.2020.2978645
    M. M. Mohammed, A. Badr, and M. B. Abdelhalim, “Image classification and retrieval using optimized pulse-coupled neural network,” Expert Systems with Applications, vol. 42, no. 11, pp. 4927–4936, 2015. doi: 10.1016/j.eswa.2015.02.019
    Y. C. Chen, F. Y. Huang, B. Q. Liu, et al., “Significant obstacle location with ultra-wide FOV LWIR stereo vision system,” Optics and Lasers in Engineering, vol. 129, article no. 106076, 2020. doi: 10.1016/j.optlaseng.2020.106076
    S. Zhang, B. Q. Liu, F. Y. Huang, et al., “Super wide field of view staring infrared imaging technology and its application,” Laser & Infrared, vol. 46, no. 10, pp. 1176–1182, 2016. doi: 10.3969/j.issn.1001-5078.2016.10.002
    S. W. Zhang, X. N. Zhang, Z. Y. Wu, et al., “Research on asphalt mixture injury digital image based on enhancement and segmentation processing technology,” Applied Mechanics and Materials, vol. 470, pp. 832–837, 2013. doi: 10.4028/www.scientific.net/amm.470.832
    M. Lee, H. Kim, and J. Paik, “Correction of barrel distortion in fisheye lens images using image-based estimation of distortion parameters,” IEEE Access, vol. 7, pp. 45723–45733, 2019. doi: 10.1109/ACCESS.2019.2908451
    X. R. Mao, K. M. Liu, L. Y. Wang, et al., “Image distortion correction algorithm based on FPGA,” Journal of Applied Optics, vol. 41, no. 1, pp. 86–93, 2020. (in Chinese) doi: 10.5768/JAO202041.0102004
    K. Koolstra, T. O’Reilly, P. Börnert, et al., “Image distortion correction for MRI in low field permanent magnet systems with strong B0 inhomogeneity and gradient field nonlinearities,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 34, no. 4, pp. 631–642, 2021. doi: 10.1007/s10334-021-00907-2
    Y. C. Chen, C. Deng, Y. Zhang, et al., “Ultra-wide FOV infrared image distortion correction based on accurate model and back-projection,” Semiconductor Optoelectronics, vol. 42, no. 4, pp. 546–550, 2021. (in Chinese) doi: 10.16818/j.issn1001-5868.2021.04.019
    W. Guan, Z. L. Wang, Z. Q. Wang, et al., “Precise axis centering calibration technology for optical lens,” Journal of Applied Optics, vol. 39, no. 2, pp. 252–256, 2018. (in Chinese) doi: 10.5768/JAO201839.0205003
    S. S. Bafjaish, M. S. Azmi, M. N. Al-Mhiqani, et al., “Skew detection and correction of Mushaf Al-Quran script using hough transform,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 8, pp. 402–409, 2018. doi: 10.14569/IJACSA.2018.090852
    M. Y. Yu, Q. G. Zhu, and Y. J. Wang, “Correction method of track image based on Canny operator and Radon transform,” Journal of Computer Applications, vol. 37,no,S2, pp. 91–94,133, 2017. (in Chinese)
    L. Li, C. D. Tan, M. J. Liao, et al., “Analytic reconstruction for parallel translational computed tomography based on radon inverse transform,” Acta Optica Sinica, vol. 41, no. 6, article no. 0611003, 2021. (in Chinese) doi: 10.3788/AOS202141.0611003
    M. Li, Z. L. Su, and D. S. Zhang, “Methods for digital image correlation measurement with camera shake correction based on affine transformation,” Acta Optica Sinica, vol. 41, no. 9, article no. 0912002, 2021. (in Chinese) doi: 10.3788/AOS202141.0912002
    X. J. Xu, X. F. Wang, W. Q. Lu, et al., “Development of part contour recognition system based on edge detection,” Journal of Mechanical & Electrical Engineering, vol. 36, no. 2, pp. 201–205, 2019. (in Chinese) doi: 10.3969/j.issn.1001-4551.2019.02.017
    K. Javed and F. Shafait, “Real-time document localization in natural images by recursive application of a CNN,” in Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, pp.105–110, 2017.
    H. Yoo and K. Jun, “Deep corner prediction to rectify tilted license plate images,” Multimedia Systems, vol. 27,no,4, pp. 779–786, 2021. doi: 10.1007/s00530-020-00655-8
    V. Abolhasannejad, X. M. Huang, and N. Namazi, “Developing an optical image-based method for bridge deformation measurement considering camera motion,” Sensors, vol. 18, no. 9, article no. 2754, 2018. doi: 10.3390/s18092754
    S. Yoneyama, A. Kitagawa, S. Iwata, et al., “Bridge deflection measurement using digital image correlation,” Experimental Techniques, vol. 31, no. 1, pp. 34–40, 2007. doi: 10.1111/j.1747-1567.2006.00132.x
    B. W. Jo, Y. S. Lee, J. H. Jo, et al., “Computer vision-based bridge displacement measurements using rotation-invariant image processing technique,” Sustainability, vol. 10, no. 6, article no. 1785, 2018. doi: 10.3390/su10061785
    H. Y. Xu, X. L. Xu, and Y. B. Zuo, “Applying morphology to improve Canny operator's image segmentation method,” The Journal of Engineering, vol. 2019, no. 23, pp. 8816–8819, 2019. doi: 10.1049/joe.2018.9113
    S. J. Gong, G. Q. Li, Y. J. Zhang, et al., “Application of static gesture segmentation based on an improved canny operator,” The Journal of Engineering, vol. 2019, no. 15, pp. 543–546, 2019. doi: 10.1049/joe.2018.9377
    P. Kanchanatripop and D. F. Zhang, “Adaptive image edge extraction based on discrete algorithm and classical canny operator,” Symmetry, vol. 12, no. 11, article no. 1749, 2020. doi: 10.3390/sym12111749
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(17)  / Tables(4)

    Article Metrics

    Article views (142) PDF downloads(22) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint